Gordon Growth Model (GGM)

  • The GGM is a variation on the standard DDM that allows the analyst to assume that dividends will grow in perpetuity at a constant rate.

V0 = Div1 /(rce - gdiv)

  • Div1 = D0 * (1 + gdiv) = future period dividend payment

  • rce = by now you should know this!

    In an exam problem CFA might make you derive the required return on common equity via CAPM.

  • gdiv = growth rate of the dividend

  • Note that in order for GGM to "work", the required return on common equity must be greater than the expected growth rate of the dividend.

  • GGM can also be used to value preferred stocks, whose dividend payments are fixed.

Preferred Stock V0 = Pref Div /r preferred stock

  • GGM can be appropriate when:

  • The analyst is looking at broad equity indexes.

  • The analyst is valuing steadily growing companies that pay dividends.

  • GGM has drawbacks of:

  • Being incredibly sensitive to small changes in the model inputs.

  • An inability to value companies that do not pay dividends.

  • An inability to value companies whose growth is not stable.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $39 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.